Why do we want to plot data?

• we are human beings – we are pattern recognizers
• we can see things we are not able to grasp from data
• good to explore a dataset and look for regularities
• good to convey a clear message
• to have fun
• (to show your colleagues how nice your plot is)

Looking at the data as a first step of analysis is always a good idea

• data could look similar at a first glance
• and even have similar descriptive statistics (i.e. mean, variance)
• but still be very different in practice

A striking example of this is the “Datasaurus dozen”: a dull an not impressive dataset.

• data contains vars x and y, over 13 different conditions
• import the data (it is in data/DatasaurusDozen.tsv) and compute mean and st.dev. by dataset

But if you plot it, you’ll see stark differences

Plotting allows one to convey a lot of information in a compact way

• humans are pattern recognizers
• several geometric objects can convey meaning
  • position (x,y)
  • color
  • size
  • shape
• you can combine multiple plots to create infographics (cool!)

• It is important to make good plots

• i.e., plots that look good…

• …and are honest to the data

• it is very easy to hide the message rather than highlighting it

• it is very easy to mislead with a plot

• so let’s start with a gallery of bad plots. Can you guess why they are bad?

• know your data
• think before you hit the enter button
• sketch on paper first
• be honest
• draw your axis first
• choose your visualization wisely
• a good plot gives lots of precise information in a concise way.

Examples:

We will start by using the built-in dataset mpg

mpg

## # A tibble: 234 × 11
##    manufacturer model      displ  year   cyl trans drv     cty   hwy fl    class
##    <chr>        <chr>      <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
##  1 audi         a4           1.8  1999     4 auto… f        18    29 p     comp…
##  2 audi         a4           1.8  1999     4 manu… f        21    29 p     comp…
##  3 audi         a4           2    2008     4 manu… f        20    31 p     comp…
##  4 audi         a4           2    2008     4 auto… f        21    30 p     comp…
##  5 audi         a4           2.8  1999     6 auto… f        16    26 p     comp…
##  6 audi         a4           2.8  1999     6 manu… f        18    26 p     comp…
##  7 audi         a4           3.1  2008     6 auto… f        18    27 p     comp…
##  8 audi         a4 quattro   1.8  1999     4 manu… 4        18    26 p     comp…
##  9 audi         a4 quattro   1.8  1999     4 auto… 4        16    25 p     comp…
## 10 audi         a4 quattro   2    2008     4 manu… 4        20    28 p     comp…
## # … with 224 more rows

• model : model name
• displ : engine displacement, in litres
• year : year of manufacture
• cyl : number of cylinders
• trans : type of transmission
• drv : f = front-wheel drive, r = rear wheel drive, 4 = 4wd
• cty : city miles per gallon
• hwy : highway miles per gallon
• fl : fuel type
• class : “type” of car

skimr::skim(mpg)

Variable type: character

Variable type: numeric

Advantages of ggplot2

• consistent underlying grammar of graphics (Wilkinson, 2005)
• plot specification at a high level of abstraction
• very flexible
• mature and complete graphics system
• theme system for polishing plot appearance
• many users, active, fast & competent support

The basic idea: independently specify plot building blocks and combine them to create just about any kind of graphical display you want. Building blocks of a graph include:

• data

• aesthetic mapping

• geometric object

• statistical transformations

• scales

• coordinate system

• position adjustments

• faceting

As in a grammar the minimal sentence is a subject in a plot the minimal object is data

ggplot(mpg)

In a grammar, you need a verb. In plots, this is axis

p <- ggplot(mpg, aes(x = displ, y = hwy))
p

Still no plot generated!

But you also need an object. In ggplot, this is geoms

p + geom_point()

But you also need an object. In ggplot, this is geoms

p + geom_smooth()

You can add (+) as many geoms as you wish

p + geom_smooth()+geom_point()

• once you get the main idea, adding things is easy
• a plot is a sentence made with data
• you add layers with +
• as you would add words to a sentence
• as in grammar you use adjectives to give more nuanced meaning, in plots you could use + to add color, fill, size, shape, etc…

p + geom_point(aes(color=class))

p + geom_point(aes(size=cyl))

p + geom_point(aes(size = cyl, color=class))

p + geom_point(aes(shape=fl))

p + geom_point(aes(color=manufacturer, shape =fl, size = cyl))

• ggplot works like a grammar
• start with ggplot()
• first argument is “the subject”, i.e. data: ggplot(df, ...)
• then you map variables to aesthetics (x, y, color, fill, shape, size, …)
• ggplot(df, aes(dimension = variable))
• then you add (+) meaning with geometric objects: geom_*
• + geom_line()
• notes:
  • geoms inherit the aes of the plot if not specified
  • all variables mapped to aes vary with the data

• sometimes sentences become a bit too long
• it is useful to split them up in shorter sentences
• for instance, you could first talk about a car, then another one
• in plots, you can split up the plot along a variable
• so that one plot is drawn for each level of a given variable, say type of fuel

p + geom_point(aes(color=manufacturer, size = cyl))+facet_grid(.~fl)

A ggplot is made up of

• data (subject)
• axis (verb)
• geoms (object)
• aesthetic layers (size, fill color, shape, label, …)
• facets (splitting sentences)

And then you can change how things look and behave: - coordinate functions (changing the axis appearance and type) - scale functions (changing the appearance of the geoms) - theme functions (changing the appearance of the plot itself)

Plot types depend on the variable type

• one-variable plots, discrete variable: barplot
• one-variable plots, continuous variable: distribution, density

• let’s look at the drive type of the cars: front, rear, or 4wd

p <- ggplot(mpg, aes(drv))
p + geom_bar()

• not so fancy. should we add color?

p <- ggplot(mpg, aes(drv))
p + geom_bar(aes(color=drv))

• ups. Maybe we meant fill?

p <- ggplot(mpg, aes(drv))
p + geom_bar(aes(fill=drv))

• what if we cross it with another variable?

p <- ggplot(mpg, aes(drv))
p + geom_bar(aes(fill=class))

• By default stacked. How to unstack?

p <- ggplot(mpg, aes(drv))
p + geom_bar(aes(fill=class), position = position_dodge())

• By default stacked. How to show relative weight?

p <- ggplot(mpg, aes(drv))
p + geom_bar(aes(fill=class), position = position_fill())

• When the variable is continuous, it makes more sense to show distributions

p <- ggplot(mpg, aes(hwy))
p + geom_histogram()

p + geom_histogram(bins = 10)

p + geom_histogram(bins = 100)

p + geom_dotplot(binwidth = 0.5)

p + geom_density()

p + geom_density(adjust = 3)

p + geom_density(adjust = 0.5)

Plot types depend on the variable type

• both vars continuous: scatter, smooth
• one continuous, one discrete: columns (i.e., bars), boxplot, violins
• both discrete: count

if two variables are continuous, your choice is scatter

p <- ggplot(mpg, aes(x = cty, y = hwy))
p + geom_point()

still, you might just want to show the general tendency

p + geom_smooth()

or both

p + geom_smooth() + geom_point()

one variable discrete, the other continuous (note: it needs a summarise())

mpg %>% group_by(manufacturer) %>% summarise(n = n()) %>% 
ggplot(aes(manufacturer, n))+
  geom_col()

the above could have been easily done with geom_bar (that counts for us)

mpg %>% ggplot(aes(manufacturer))+
  geom_bar()

but columns give you more options, since now you condition on a proper variable (n). For instance: order by n

mpg %>% group_by(manufacturer) %>% summarise(n = n()) %>% 
ggplot(aes(reorder(manufacturer, -n), n))+
  geom_col()

boxplots show a distribution but can do so over different levels of a categorical var

mpg %>% ggplot(aes(drv, hwy))+
  geom_boxplot()

boxplots are bulky and only show relevant info. Want full distribution? Use violins

mpg %>% ggplot(aes(drv, hwy))+
  geom_violin()

remember: all is modular. We can always add color, fill…

mpg %>% ggplot(aes(drv, hwy, color = drv, fill = drv))+
  geom_violin()

remember: all is modular. …facets

mpg %>% ggplot(aes(drv, hwy, color = drv, fill = drv))+
  geom_violin()+
  facet_grid(.~year)

if both variables are categorical, you can count their cross-tabulation

mpg %>% ggplot(aes(fl, drv))+
  geom_count()

Plot types depend on the variable type

• all continuous: contour plot (think: elevation in maps)
• some discrete: tile

two variables define the x,y grid. A third defines the color of the cell. city consumption by year and drive (note: usually requires summarise())

mpg %>% group_by(year, drv) %>% summarise(n = n()) %>% 
  ggplot(aes(x = drv, y = year, fill = n)) +  geom_tile()

• the ggplot cheatsheet is your friend (Help -> cheatsheets)
• stack overflow helps out for trickier questions
• not feeling inspired? 50 cool visualisations here: http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
• need a complete tour of possibilities? https://www.r-graph-gallery.com/